It is well documented and widely accepted in the utility industry to use dissolved gas analysis (DGA) on dielectric insulating oil of power transformers, load tap changers, circuit breakers and voltage regulators as a tool for detecting problems and assessing an overall health of the oil insulated electrical apparatus. Oil quality analysis has been utilized as well with power transformers. Industry accepted published limits for DGA and oil quality currently exist for transformers (ref. IEEE C57.106). However, these prior approaches have failed to be applicable to other electrical components that include arcing, substantial exposure to humidity, moving parts, resistive contacts, and/or the like that result in insulating oil degradation, formation of sludge, resistive contact filming, heating, coking and/or like problems. Moreover these prior approaches typically only determine issues in the electrical components at a time period close to failure. For example, DGA applied to load tap changers has been recognized as a valid tool for detecting problems after a fault has already begun to occur in the load tap changer. Currently, utilities typically do not apply oil quality in conjunction with DGA for certain electrical components, such as load tap changers, and moreover there are no published industry accepted limits, available for certain electrical components such as load tap changers.
Additionally, with constantly decreasing budgets, utilities are faced with the need to extend maintenance cycles which require de-energized internal inspections while not reducing the reliability. This has resulted in a shift from time based to predictive based maintenance. While DGA is accepted as a diagnostic tool for identifying electrical components, such as load tap changers (LTCs) with internal faults, the detection occurs only if the oil is sampled and analyzed at the right time and after the internal fault has already developed. Using only DGA to determine LTCs already in failure mode leads to higher (reactive) maintenance costs, unplanned outages, and poor utilization of minimal maintenance personnel constantly “fighting fires.”
Accordingly, there is a need for a system and process to more accurately detect early-stage problems with electrical components utilizing a new combination of analytical approaches.